




"""simple docstring"""

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   Tuple							,    _snake_case				:   Union[str, Any]							,    _snake_case				:   Dict							,    _snake_case				:   Any							,    _snake_case				:   int = None							,    )       ->   int:






      '''simple docstring'''


      _A     =  {}

      if train_file is not None:
            _A     =  [train_file]
      if eval_file is not None:
            _A     =  [eval_file]
      if test_file is not None:
            _A     =  [test_file]

      _A     =  datasets.load_dataset('csv'							,    data_files=_snake_case				)
      _A     =  list(ds[list(files.keys()				)[0]].features.keys()				)
      _A     =  features_name.pop(_snake_case				)
      _A     =  list(set(ds[list(files.keys()				)[0]][label_name]				)				)
      _A     =  {label: i for i, label in enumerate(_snake_case				)}
      _A     =  tokenizer.model_input_names
      _A     =  {}

      if len(_snake_case				) == 1:
            for k in files.keys():
                  _A     =  ds[k].map(
                      lambda _snake_case				: tokenizer.batch_encode_plus(
                          example[features_name[0]]							,    truncation=_snake_case							,    max_length=_snake_case							,    padding='max_length'				)							,    batched=_snake_case							,    )
      elif len(_snake_case				) == 2:
            for k in files.keys():
                  _A     =  ds[k].map(
                      lambda _snake_case				: tokenizer.batch_encode_plus(
                          (example[features_name[0]], example[features_name[1]])							,    truncation=_snake_case							,    max_length=_snake_case							,    padding='max_length'							,    )							,    batched=_snake_case							,    )

      def gen_train():
            for ex in transformed_ds[datasets.Split.TRAIN]:
                  _A     =  {k: v for k, v in ex.items() if k in input_names}
                  _A     =  labelaid[ex[label_name]]
                  yield (d, label)

      def gen_val():
            for ex in transformed_ds[datasets.Split.VALIDATION]:
                  _A     =  {k: v for k, v in ex.items() if k in input_names}
                  _A     =  labelaid[ex[label_name]]
                  yield (d, label)

      def gen_test():
            for ex in transformed_ds[datasets.Split.TEST]:
                  _A     =  {k: v for k, v in ex.items() if k in input_names}
                  _A     =  labelaid[ex[label_name]]
                  yield (d, label)

      _A     =  (
          tf.data.Dataset.from_generator(
              _snake_case							,    ({k: tf.intaa for k in input_names}, tf.intaa)							,    ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))							,    )
          if datasets.Split.TRAIN in transformed_ds
          else None
      )

      if train_ds is not None:
            _A     =  train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

      _A     =  (
          tf.data.Dataset.from_generator(
              _snake_case							,    ({k: tf.intaa for k in input_names}, tf.intaa)							,    ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))							,    )
          if datasets.Split.VALIDATION in transformed_ds
          else None
      )

      if val_ds is not None:
            _A     =  val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

      _A     =  (
          tf.data.Dataset.from_generator(
              _snake_case							,    ({k: tf.intaa for k in input_names}, tf.intaa)							,    ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))							,    )
          if datasets.Split.TEST in transformed_ds
          else None
      )

      if test_ds is not None:
            _A     =  test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

      return train_ds, val_ds, test_ds, labelaid


a							      =     logging.getLogger(__name__)





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	int     =      field(metadata={'''help''': '''Which column contains the label'''}				)
 UpperCAmelCase       :	str     =      field(default=_A     ,      metadata={'''help''': '''The path of the training file'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(default=_A     ,      metadata={'''help''': '''The path of the development file'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(default=_A     ,      metadata={'''help''': '''The path of the test file'''}				)
 UpperCAmelCase       :	int     =      field(
     default=128     ,      metadata={
         '''help''': (
             '''The maximum total input sequence length after tokenization. Sequences longer '''
             '''than this will be truncated, sequences shorter will be padded.'''
         )
     }     ,      )
 UpperCAmelCase       :	bool     =      field(
     default=_A     ,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}				)





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	str     =      field(
     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(
     default=_A     ,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(
     default=_A     ,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}				)
 UpperCAmelCase       :	bool     =      field(default=_A     ,      metadata={'''help''': '''Set this flag to use fast tokenization.'''}				)
 # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
 # or just modify its tokenizer_config.json.
 UpperCAmelCase       :	Optional[str]     =      field(
     default=_A     ,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,      )






def 		_snake_case	(   )       ->   Optional[int]:






      '''simple docstring'''


      _A     =  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
      _A     =  parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir				)
          and os.listdir(training_args.output_dir				)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                ' --overwrite_output_dir to overcome.'				)

      # Setup logging
      logging.basicConfig(
          format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,    datefmt='%m/%d/%Y %H:%M:%S'							,    level=logging.INFO							,    )
      logger.info(
          F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1				)}, '''
          F'''16-bits training: {training_args.fpaa}'''				)
      logger.info(F'''Training/evaluation parameters {training_args}'''				)

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      _A     =  AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,    cache_dir=model_args.cache_dir							,    )

      _A     =  get_tfds(
          train_file=data_args.train_file							,    eval_file=data_args.dev_file							,    test_file=data_args.test_file							,    tokenizer=_snake_case							,    label_column_id=data_args.label_column_id							,    max_seq_length=data_args.max_seq_length							,    )

      _A     =  AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path							,    num_labels=len(_snake_case				)							,    labelaid=_snake_case							,    idalabel={id: label for label, id in labelaid.items()}							,    finetuning_task='text-classification'							,    cache_dir=model_args.cache_dir							,    )

      with training_args.strategy.scope():
            _A     =  TFAutoModelForSequenceClassification.from_pretrained(
                model_args.model_name_or_path							,    from_pt=bool('.bin' in model_args.model_name_or_path				)							,    config=_snake_case							,    cache_dir=model_args.cache_dir							,    )

      def compute_metrics(_snake_case				:   Any				) -> Dict:
            _A     =  np.argmax(p.predictions							,    axis=1				)

            return {"acc": (preds == p.label_ids).mean()}

      # Initialize our Trainer
      _A     =  TFTrainer(
          model=_snake_case							,    args=_snake_case							,    train_dataset=_snake_case							,    eval_dataset=_snake_case							,    compute_metrics=_snake_case							,    )

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            tokenizer.save_pretrained(training_args.output_dir				)

      # Evaluation
      _A     =  {}
      if training_args.do_eval:
            logger.info('*** Evaluate ***'				)
            _A     =  trainer.evaluate()
            _A     =  os.path.join(training_args.output_dir							,    'eval_results.txt'				)

            with open(_snake_case							,    'w'				) as writer:
                  logger.info('***** Eval results *****'				)

                  for key, value in result.items():
                        logger.info(F'''  {key} = {value}'''				)
                        writer.write(F'''{key} = {value}\n'''				)

                  results.update(_snake_case				)

      return results


if __name__ == "__main__":
       main()



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






